Sparsity Meaning: Definition, Examples, and Translations
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sparsity
[ˈspɑːsɪti ]
Definitions
in statistics
Sparsity refers to the property of having very few non-zero elements in a dataset or mathematical structure. It is commonly used in the context of sparse matrices, where most of the elements are zero.
Synonyms
infrequency, rareness, scantiness.
Which Synonym Should You Choose?
| Word | Description / Examples |
|---|---|
| sparsity |
Used when discussing the lack or thin distribution of elements in a certain area or field. Often used in scientific or technical contexts.
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| scantiness |
Often carries a negative connotation, implying an inadequacy or insufficient amount. Used mostly in critical or evaluative scenarios.
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| rareness |
Used when talking about something that is uncommon or not easily found. It can carry a positive connotation when describing something valuable or special.
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| infrequency |
Used to describe events or occurrences that do not happen often over a period of time. Mainly used in everyday language or formal writing.
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Examples of usage
- The sparsity of the data matrix allowed for efficient storage and computation.
- Sparse coding techniques take advantage of the sparsity present in the data.
in everyday language
Sparsity can refer to a state of being scarce or lacking in quantity. It often implies a sense of insufficiency or inadequacy.
Synonyms
Which Synonym Should You Choose?
| Word | Description / Examples |
|---|---|
| sparsity |
Used to describe something that is thinly scattered or few and spread out, often in a physical or spatial sense.
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| scarcity |
Implies a limited availability of resources, often leading to higher demand or value. It can be used in economic contexts or everyday situations.
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| dearth |
Refers to a severe lack or shortage of something, often implying a negative consequence or problem due to the shortage.
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| paucity |
Used to describe a small or insufficient quantity of something. It often has a formal or academic tone.
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Examples of usage
- The sparsity of resources in the region led to widespread poverty.
- The sparsity of options left us with limited choices.
Translations
To see the translation, please select a language from the options available.
Interesting Facts
Science
- In ecology, sparsity can describe areas that lack vegetation, showcasing the impact of climate or human activity.
- In statistics, sparsity refers to datasets that have many zero values, indicating information is missing or not present.
- Researchers explore sparsity in machine learning, where simpler models can often achieve better results with fewer parameters.
Economics
- In economics, the concept of sparsity relates to resource allocation, where limited resources lead to careful distribution.
- Sparsity can drive innovation, as businesses strive to do more with less, fostering creativity and efficiency.
- The famous 'scarcity principle' states that limited availability increases perceived value.
Cultural References
- Minimalistic design in art and architecture reflects the principle of sparsity, focusing on simplicity and open space.
- Literature often uses sparsity to evoke feelings of loneliness or isolation through descriptions of empty landscapes.
- In fashion, a trend for 'less is more' embraces the idea of sparsity, promoting a smaller wardrobe with versatile staples.
Psychology
- Studies show that experiencing sparsity in one's environment can lead to increased problem-solving and adaptability.
- The psychological effects of scarcity can create stress, influencing decision-making and behavior patterns.
- Behavioral economics often examines how individuals react to scarce resources, impacting choices related to spending and saving.
Origin of 'sparsity'
Main points about word origin
- Originates from the Latin word 'sparsus', meaning 'scattered or thinly spread'.
- The term is often used in scientific contexts to describe a lack of density.
- First recorded use in English dates back to the mid-17th century.
The word 'sparsity' originated from the Latin word 'sparsum', which means scattered. It has been used in mathematics and statistics to describe datasets with a low density of non-zero elements. The concept of sparsity has gained importance in various fields such as machine learning, signal processing, and optimization.